code: def get_prediction(self, states):
      tf_input_state = self.prepare_batch_for_inference(states, "main")
      return self.networks["main"].online_network.predict(
          tf_input_state
      )[1:]
  
imports: from typing import Union
  import numpy
  from rl_coach.agents.policy_optimization_agent import PolicyOptimizationAgent
  from rl_coach.architectures.embedder_parameters import InputEmbedderParameters
  from rl_coach.architectures.head_parameters import ACERPolicyHeadParameters, QHeadParameters
  from rl_coach.architectures.middleware_parameters import FCMiddlewareParameters
  from rl_coach.base_parameters import AlgorithmParameters, NetworkParameters, AgentParameters
  from rl_coach.core_types import Batch
  from rl_coach.exploration_policies.categorical import CategoricalParameters
  from rl_coach.memories.episodic.episodic_experience_replay import EpisodicExperienceReplayParameters
  from rl_coach.spaces import DiscreteActionSpace
  from rl_coach.utils import eps, last_sample
label_desc: Use image node_id 5 for calling the ACERAgent obj's underlying member method code with example usage: obj.get_prediction(states) and returns: self